Twelve essays explore what bearing empirical findings might have on philosophical concerns about counterfactuals and causation, and how, in turn, work in philosophy might help clarify issues in empirical work on the relationships between causal and counterfactual thought.

One philosophical approach to causation sees counterfactual dependence as the key to the explanation of causal facts: for example, events c (the cause) and e (the effect) both occur, but had c not occurred, e would not have occurred either. The counterfactual analysis of causation became a focus of philosophical debate after the 1973 publication of the late David Lewis's groundbreaking paper, "Causation," which argues against the previously accepted "regularity" analysis and in favor of what he called the "promising alternative" of the counterfactual analysis. Thirty years after Lewis's paper, this book brings together some of the most important recent work connecting—or, in some cases, disputing the connection between—counterfactuals and causation, including the complete version of Lewis's Whitehead lectures, "Causation as Influence," a major reworking of his original paper. Also included is a more recent essay by Lewis, "Void and Object," on causation by omission. Several of the essays first appeared in a special issue of the Journal of Philosophy, but most, including the unabridged version of "Causation as Influence," are published for the first time or in updated forms. Other topics considered include the "trumping" of one event over another in determining causation; de facto dependence; challenges to the transitivity of causation; the possibility that entities other than events are the fundamental causal relata; the distinction between dependence and production in accounts of causation; the distinction between causation and causal explanation; the context-dependence of causation; probabilistic analyses of causation; and a singularist theory of causation.

Presents a comprehensive account of how the mind causes things to happen in the physical world. This book is also available as Open Access.

In Making Things Happen, James Woodward develops a new and ambitious comprehensive theory of causation and explanation that draws on literature from a variety of disciplines and which applies to a wide variety of claims in science and everyday life. His theory is a manipulationist account, proposing that causal and explanatory relationships are relationships that are potentially exploitable for purposes of manipulation and control. This account has its roots in the commonsense idea that causes are means for bringing about effects; but it also draws on a long tradition of work in experimental design, econometrics, and statistics. Woodward shows how these ideas may be generalized to other areas of science from the social scientific and biomedical contexts for which they were originally designed. He also provides philosophical foundations for the manipulationist approach, drawing out its implications, comparing it with alternative approaches, and defending it from common criticisms. In doing so, he shows how the manipulationist account both illuminates important features of successful causal explanation in the natural and social sciences, and avoids the counterexamples and difficulties that infect alternative approaches, from the deductive-nomological model onwards. Making Things Happen will interest philosophers working in the philosophy of science, the philosophy of social science, and metaphysics, and as well as anyone interested in causation, explanation, and scientific methodology.

This book is about making machine learning models and their decisions interpretable. After exploring the concepts of interpretability, you will learn about simple, interpretable models such as decision trees, decision rules and linear regression. Later chapters focus on general model-agnostic methods for interpreting black box models like feature importance and accumulated local effects and explaining individual predictions with Shapley values and LIME. All interpretation methods are explained in depth and discussed critically. How do they work under the hood? What are their strengths and weaknesses? How can their outputs be interpreted? This book will enable you to select and correctly apply the interpretation method that is most suitable for your machine learning project.

What is the nature of causation? How is causation linked with explanation? And can there be an adequate theory of explanation? These questions and many others are addressed in this unified and rigorous examination of the philosophical problems surrounding causation, laws and explanation. Part 1 of this book explores Hume's views on causation, theories of singular causation, and counterfactual and mechanistic approaches. Part 2 considers the regularity view of laws and laws as relations among universals, as well as recent alternative approaches to laws. Part 3 examines the issues arising from deductive-nomological explanation, statistical explanation, the explanation of laws and the metaphysics of explanation. Accessible to readers of all levels, this book provides an excellent introduction to one of the most enduring problems of philosophy.

Causation is at once familiar and mysterious—we can detect its presence in the world, but we cannot agree on the metaphysics of the causal relation. L. A. Paul and Ned Hall guide the reader through the most important philosophical treatments of causation, and develop a broad and sophisticated understanding of the issues under debate.